This document discusses computer aided formulation development and optimization techniques. It covers topics like optimization parameters, response surface curves, experimental designs, and factorial designs. Specifically, it defines optimization as making a design or system as effective as possible. It also describes types of experimental designs like factorial designs, which study the effects of varying multiple factors simultaneously. Full and fractional factorial designs are explained, with fractional designs requiring fewer runs by testing only a subset of the full factorial design combinations.

1. COMPUTER AIDED
FORMULATION DEVELOPMENT
• SIDDU K M
• M PHARM
• DEPARTMENT OF PHARMACEUTICS
• AL AMEEN COLLEGE OF PHARMACY, BENGALURU

2. CONTENTS
• OPTIMIZATION
• OPTIMIZATION PARAMETRES
• RESPONSE SURAFCE CURVES
• EXPERIMENTAL DESIGNS
• TYPES OF EXPERIMENTAL DESIGNS
• FACTORIAL DESIGNS

3. OPTIMIZATION
• Optimization technique is a rational approach for selecting the
excipients, their concentrations and process conditions for obtaining
the best possible product satisfying the quality characteristics.
• Optimization is an act, process or methodology of making design,
system or decisions as fully perfect, functional or as effective as
possible.

4. Optimization Parameters

7. Response Surface Curve

8. Experimental Designs
• Modern pharmaceutical optimization involves systematic design of
experiments (DoE) to improve formulation irregularities.
• Design of Experiment (DoE) may be defined as the strategy for setting
up experiments in such a manner that the required information is
obtained as efficiently & precisely as possible.

9. Types of Experimental Designs
1. Completely randomized designs
2. Randomized block designs
3. Factorial designs
a) Full factorial
b) Fractional factorial
4. Response surface designs
a) Central composite designs
b) Box behnken designs
5. Adding centre points
6. Three level full factorial design

10. Factorial Designs
• A factorial design is type of design of experiment that lets us study
the effects that several factors can have on a response.
• When conducting an experiment, varying the levels of all factors at
the same time instead of one at a time lets you study the interactions
between factors.

11. Full Factorial Design
• A full factorial design is a design in which researchers measure
responses at all combinations of the factor levels.
• The number of runs necessary for a full factorial design is LF
L = number of levels
F= number of factors
• For example, 2 factors with 2 levels – FD
A 2 level full factorial design with 2 factors require 22 = 4 runs

12. Two variable & two levels factorial design
No of
Expts
Treatment
combinations
Level of X1 Level of X2 Y response
1 1 -1 -1 Y1
2 X1 1 -1 Y2
3 X2 -1 1 Y3
4 X1X2 1 1 Y4

13. Fractional Factorial Design
• A fractional factorial design is a design is a design in which
experiments conduct only a selected subsets or fraction of the runs in
the full factorial design.
• Fractional factorial designs are a good choice when resources are
limited or the number of factors in the design is large because they
use fewer runs then the full factorial designs.
• A fractional factorial design uses a subset of a full factorial design
• The fractional factorial design that includes half of the runs that a full
factorial design has

14. • The number of runs necessary for a fractional factorial design is LF-1
• Half factorial with 2 levels & 4 factors
24-1 = 8 runs
Full factorial design ½ fraction factorial design

15. • The full factorial design contains twice as many design points as the ½
fraction design.
• The response is only measured at four of the possible eight factorial
portion of the design.

16. THANK YOU